Boosting Numerical Efficiency with Low Discrepancy Sampling: Enhancing Estimation and Integration in Diverse Fields from Fred Hickernell and the QMCPy Library

This talk will discuss how low discrepancy sampling can improve the efficiency of estimating an expectation or integral, which is an important topic in various fields such as high energy physics, Bayesian inference, image rendering, quantitative finance, and uncertainty quantification. The talk will emphasize low discrepancy sampling and its ability to improve the efficiency of calculations. The topic of improving efficiency with transformations of the integral, will also be discussed. The talk will also cover how data-driven error bounds can advise users when to stop simulating. The presentation will include illustrations of low discrepancy sampling using the QMCPy library.

This talk starts at 10:30am Thursday May 18th and ends at 11:30 am CST

It will be held in Building 362 Room F108 at Argonne national laboratories.

To Join the online meeting go to link:

Meeting ID: 160 587 0003

password: 559683


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